Both ON- and OFF- neuronal responses, and perceptual responses to increments and decrements, exhibit strong asymmetries, consistent with the idea that darkness induction is inherently stronger than brightness induction. These asymmetries are also observed in lightness, e.g. staircase-Gelb displays, when the Gelb papers are viewed against dark versus light backgrounds (Cataliotti & Gilchrist, 1995). Here, I attempt to found a computational lightness theory that properly incorporates light-dark asymmetries, and includes both low- and mid-level spatial context effects. I begin at the lowest level: with Stevens' power law, which models the brightness (or darkness) of an isolated increment (decrement) viewed against a homogeneous background. The brightness exponent is 1/3; the darkness exponent is 1. To model matching data, it is convenient to express the power law in logarithmic form. The exponent then becomes a weight multiplying the step in log luminance from background to target. We know from past work that the lightness of a disk surrounded by one or more concentric annuli can be modeled as a weighted sum of edge-based induction effects, where edge weights decline with distance from the target. Combining this result with the assumption that the weights associated with edges whose light sides point towards the target are always 1/3 and the weights associated with edges whose dark sides point towards the target are 1 (after controlling for distance) yields an edge integration model that explains quantitative data on either brightness or lightness, depending on context. To quantitatively model the staircase-Gelb data, these low-level factors must be supplemented with an additional, image segmentation, principle: only edges between the Gelb papers and their common background participate in the edge integration process (Rudd, J. Vision, in press). The theory differs from other lightness theories in that the light-dark asymmetry results from low-level factors, rather than highest luminance anchoring.